Spirometry is the most widely employed objective measure of lung function and is central to the diagnosis and management of chronic lung diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis. During an existing spirometry test, a patient forcefully exhales through a flow-monitoring device (e.g., through a tube or mouthpiece) that measures instantaneous flow and cumulative exhaled volume. Spirometry is typically performed in medical offices and clinics using conventional spirometers. Spirometry performed using a portable device is, however, slowly gaining acceptance. Spirometry performed with a portable device allows a patient and/or physician to more regularly monitor the patient's lung function for trends and detect changes in lung function that may need evaluation and/or treatment. Spirometry performed with a portable device may lead to earlier diagnosis of impaired lung function and may thereby result in earlier treatment of exacerbations, more rapid recovery, reduced health care costs, and/or improved outcomes.
A standard spirometer measures flow rate of air as it passes through a mouthpiece. The measured flow can be integrated to produce Flow vs. Time (FT), Volume vs. Time (VT), and/or Flow vs. Volume (FV) plots of the expiration. An example FV plot is shown in FIG. 1, which also illustrates the following spirometry parameters:
(1) Forced Vital Capacity (FVC) is the total expelled volume during the expiration,
(2) Forced Expiratory Volume in one second (FEV1) is the volume exhaled in the first second,
(3) FEV1/FVC is simply the ratio of the aforementioned parameters, and
(4) Peak Expiratory Flow (PEF) is the maximum flow velocity reached during the test.
The most common clinically-reported measures are FEV1, FVC, and FEV1/FVC, as they are used to quantify the degree of airflow limitation in chronic lung diseases such as asthma, COPD, and cystic fibrosis. In general, a healthy result is >80% of the predicted value based on height, age, and gender (see, e.g., Knudson, R. J., Slatin, R. C., Lebowitz, M. D., and Burrows, B. The maximal expiratory flow-volume curve. Normal standards, variability, and effects of age. The American review of respiratory disease 113, 5 (1976)). Abnormal values are (see, e.g., Miller, M. R., Hankinson, J., Brusasco, V., et al. Standardisation of spirometry. The European Respiratory Journal 26, 2 (2005)):                Mild Lung Dysfunction: 60-79%        Moderate Lung Dysfunction: 40-59%        Severe Lung Dysfunction: below 40%        
Spirometry based diagnosis is, however, more complicated than simple benchmarking. Additionally, the shape of the flow curve is subjectively evaluated by a pulmonologist, who examines the descending portion of the Flow vs. Volume curve (i.e., the portion after PEF in FIG. 1). A linear slope illustrated by first FV plot 12 is indicative of the absence of airflow limitation (i.e., normal lung function). A concave or “scooped” slope illustrated by second FV plot 14 is indicative of airflow limitation (e.g., asthma or COPD) due to differing time constants of exhaled air in different parts of the lung. Third FV plot 16 is suggestive of restrictive lung disease such as that caused by respiratory muscle weakness or pulmonary fibrosis; it can be seen as a slight bowing of the curve, a plateau, and/or a decreased FVC.
Existing Spirometry Devices
Existing spirometers are generally flow based and measure the instantaneous exhaled flow (e.g., liters/sec.). There are four prevalent types of flow-based spirometers: pneumotachographs, turbines, anemometers, and ultrasounds. Pneumotachs measure the pressure differential across a membrane as the subject exhales. These devices are affected by humidity and temperature and require daily calibration. Pneumotachs are the most prevalent spirometers in medical offices and clinics because of their accuracy.
High-end clinical spirometers can cost upwards of $5000 USD and be comparable in size to a small refrigerator. The patient sits inside an enclosure that controls humidity, temperature, and oxygen levels. Portable, ATS-endorsed spirometers (about the size of a laptop) generally cost between $1,000-$4,000 USD, and although they are relatively portable compared to their counterparts, they are still bulky, complicated devices (see, e.g., FIG. 2).
Low cost peak flow meters can cost between $10-$50 USD, but typically can only measure PEE Such low cost meters are generally about the size of a baseball and typically use a mechanical apparatus without any electronics. PET in isolation, however, is generally considered irrelevant by pulmonologists (see, e.g., Pesola, G., O'Donnell, P., and Jr, G. P. Peak expiratory flow in normals: comparison of the Mini Wright versus spirometric predicted peak flows. Journal ref Asthma, 4 (2009)). Digital home spirometers that report only FEY1 are also commercially available ($50-$200 USD); the functionality of these meters varies widely with regard to reporting and archiving of results. For example, some digital home spirometers require patients to manually record results in journals or have a USB desktop connection. Some recent digital home spirometers can connect to a mobile phone or laptop via BLUETOOTH, but are typically more expensive (e.g., $900-$3500 USD). Recently, some low-cost (approximately $100-$200 USD) BLUETOOTH spirometers have gained excitement in the mobile health community (see, e.g., Sakka, E. J., Aggelidis, P., and Psimamou, M. Mobispiro: A Novel Spirometer. In AIEDICON '10. 2010), but their contributions are mostly in the coupling of existing hardware and Android platforms and are not ATS endorsed.
Additionally, a number of applications that claim to measure aspects of lung function have recently appeared on smartphone platforms. These smartphone applications, however, are advertised as games and have disclaimers warning not to use them for medical assessment.
Mobile Phone Based Health Sensing
There are a number of healthcare sensing systems in which external sensors are connected to smartphones. For example, Poh et al. have developed a system containing electro-optic sensors worn on the earlobe to provide photoplethysmography (PPG) data on a smartphone (see, Poh, M.-Z., Swenson, N. C., and Picard, R. W. Motion-Tolerant Magnetic Earring Sensor and Wireless Earpiece for Wearable Photoplethysmography. Information Technology in Biomedicine, IEEE Transactions on 14, 3 (2010)). A number of researchers have also evaluated how multiple sensors could be connected to a smartphone via an external board to collect physiological information (see, e.g., Brunette, W., Sodt, R., Chaudhri, R., et al. The Open Data Kit Sensors Framework: Application-Level Sensor Drivers for Android. MobiSys, (2012); also see, e.g., Majchrzak, T. and Chakravorty, A. Improving the Compliance of Transplantation Medicine Patients with an Integrated Mobile System. International Conference on System Sciences, (2012)). Bishara et al. have successfully modified an existing on-device camera to perform lens-free holographic microscopy (see, Bishara, W., Su, T.-W., Coskun, A. F., and Ozcan, A. Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution. Opt. Express 18, 11 (2010)). Pamplona et al. have developed NETRA, a system that combines simple optical components, like lenses, with high-resolution LCD screens of smartphones to detect human eye impairments (see, Pamplona, V. F., Mohan, A., Oliveira, M. M., and Raskar, R. NETRA: interactive display for estimating refractive errors and focal range. SIGGRAP'10, ACM (2010)).
Researchers have also been exploring solutions that require no hardware modification. For example, Grimaldi et al. have employed a smartphone's camera and LED flashlight to measure pulse from the fingertip using photoplethysmography (see, Grimaldi, D., Kurylyak, Y., Lamonaca, F., and Nastro, A. Photoplethysmography detection by smartphone's videocamera. IDAACS, (2011)); while this requires a user to be in contact with the device, Poh et al. use a tablet's camera and blind source separation of color channels to measure pulse at a distance (see, Poh, M.-Z., McDuff, D. J., and Picard, R. W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18, 10 (2010)).
Audio Based Health Sensing
There are also several technologies that sense medically relevant quantities using a microphone. For example, using an in-ear microphone, researchers have shown that one can detect when (and sometimes what) a person is eating (see, e.g., Amft, O. and Lukowicz, P. Analysis of chewing sounds for dietary monitoring. UbiComp '05, (2005); see also, e.g., Nishimura, J. and Kuroda, T. Eating habits monitoring using wireless wearable in-ear microphone. ISWPC 2008, (2008)). Wheeze detection with in-air and throat microphones has shown promising results in diagnosing the severity of asthma (see, e.g., Homs-Corbera, A. and Fiz, J. Time-frequency detection and analysis of wheezes during forced exhalation. IEEE Transactions 51, 1 (2004)). Respiratory rate is another vital sign typically sensed with body worn (see, e.g., Alshaer, H., Fernie, G. R., and Bradley, T. D. Phase tracking of the breathing cycle in sleeping subjects by frequency analysis of acoustic data. International Journal of Healthcare Technology and Management 11, 3 (2010)) or bedside microphones (see, e.g., Kroutil, J. and Laposa, A. Respiration monitoring during sleeping. ISABEL '11, (2011)). A few systems have leveraged simple, low-cost microphones to analyze signals, such as heart rate and cough. Many systems exist that extract heart rate using a mobile phone (see, e.g., Neuman, M. R. Vital Signs: Heart Rate. Pulse, IEEE 1, 3 (2010); see also, e.g., Olmez, T. and Dokur, Z. Classification of heart sounds using an artificial neural network. Pattern Recognition Letters 24, 1-3 (2003)) and, with higher-end microphones, some systems can actually be used to detect certain audible manifestations of high blood pressure referred to as Korotkoff sounds (see, e.g., Allen, J. and Murray, A. Time-frequency analysis of Korotkoff sounds. IEE Seminar Digests 1997, 6 (1997)). The Ubicomp community has also embraced some of this work. At Ubicomp 2011, a solution that uses the microphone on the mobile phone to detect and count coughs was presented (see, e.g., Larson, E. C., Lee, T., Liu, S., Rosenfeld, M., and Patel, S. N. Accurate and Privacy Preserving Cough Sensing using a Low-Cost Microphone. UbiComp '11, (2011)).
While home spirometry is slowly gaining acceptance in the medical community because of its ability to detect pulmonary exacerbations and improve outcomes of chronic lung ailments, limitations of existing home spirometer devices are inhibiting its widespread adoption. For example, challenges currently faced by home spirometry include excessive cost, patient compliance, usability, and the ability to upload results to physicians (see, e.g., Finkelstein J, Cabrera M R, H. G. Internet-based home asthma telemonitoring: can patients handle the technology. Chest 117, 1 (2000); see also, e.g., Grzincich, G., Gagliardini, R., and Bossi, A. Evaluation of a home telemonitoring service for adult patients with cystic fibrosis: a pilot study. J. of Telemedicine, (2010)). Notably, while office-based spirometry is typically coached by a trained technician, current home spirometers have no coaching, feedback, or quality control mechanisms to ensure acceptable measurements. Accordingly, improved methods and devices for accomplishing home spirometry would be beneficial.